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Ontologies: Making Computers Smarter to Deal with Data Kei Cheung, PhD Yale Center for Medical Informatics CBB752, February 9, 2015, Yale University.

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Presentation on theme: "Ontologies: Making Computers Smarter to Deal with Data Kei Cheung, PhD Yale Center for Medical Informatics CBB752, February 9, 2015, Yale University."— Presentation transcript:

1 Ontologies: Making Computers Smarter to Deal with Data Kei Cheung, PhD Yale Center for Medical Informatics CBB752, February 9, 2015, Yale University

2 Dealing with data Science 11 February 2011: 692-729

3 Examples of Big Data Genomics and proteomics data (e.g., next generation sequencing and mass spectrometry) Earth science data (e.g., satellite images) Electronic health records Social network data (e.g., facebook, youtube, …)

4 Big Data in Genome Sciences

5 Can Google answer every question?

6 Kei (Hoi) Cheung (>20 years ago) Kei (Hoi) Cheung (more recent) Kei (Hui) Cheung Not me! I’m NOT a company! Find the most recent image of the person “Kei Hoi Cheung” Problem with Keyword Search


8 Data Science Extraction of knowledge from data (and metadata) Machine learning Natural language processing High performance computing

9 Knowledge Bases Artificial Intelligence Machine-readable (reasonable) knowledge representation Ontologies Semantic web

10 Data Science & Knowledge Base Data ScienceKnowledge Base

11 What is an ontology? An ontology is a specification of a conceptualization It is a description of the concepts and their relationships that exist for a particular domain

12 Knowledge Web

13 Knowledge Web Data Integration

14 Semantic Web: Web 3.0 The Semantic Web provides a common machine-readable ontology framework that allows data to be represented, shared and reused across application, enterprise, and community boundaries –The Semantic Web is a knowledge web of data The Semantic Web is about two things –It is about common formats for identification, representation, and integration of data drawn from diverse sources –It is also about languages for describing how the data relates to real world objects

15 Layers of the Semantic Web

16 Web 3.0: Semantic Web (Cont’d) Global identifying scheme (URI) Standard data modeling languages (RDF, RDFS, OWL) Standard query languages (SPARQL) Enabling tools/technologies (e.g., Protégé, Jena, triplestore, etc)

17 Resource Description Framework (RDF) It is a standard data model (directed acyclic graph) for representing information (metadata) about resources in the World Wide Web In general, it can be used to represent information about “things” or “resources” that can be identified (using URI’s) on the Web It is intended to provide a simple way to make statements (descriptions) about Web resources

18 Uniform Resource Identifiers (URIs) A URI is a string of characters used to identify or name a resource on the Internet. URLs (Uniform Resource Locators) are a particular type of URI, used for resources that can be accessed on the WWW (e.g., web pages) In RDF, URIs typically look like “normal” URLs, often with fragment identifiers to point at specific parts of a document: – (id for “core cell cycle protein” in Cell Cycle Ontology)

19 RDF Triple/Graph The basic information unit in RDF is an RDF statement in the form of –(subject, property, object) Each RDF statement can be modeled as a graph comprising two nodes connected by a directed arc A triple example A set of such triples can jointly form a directed labeled graph (DLG) that can in theory model a significant part of domain knowledge. An RDF graph can be represented in different formats (XML, Turtle, N3…)

20 Linking data of the same type from multiple sources is a

21 Linking data across different types

22 Named Graph located in Interacts with biordf:P05067 Meta Statement biordf:P05067foaf:kei_cheung Created by

23 Cell Cycle Ontology (CCO) ( Antezana et al, 2009, Genome Biology)

24 RDF Graph Match (SPARQL) BASE PREFIX rdfs: PREFIX ssb: SELECT ?protein_label WHERE { GRAPH { ?protein ssb:is_a ssb:CCO_B0000000. ?protein rdfs:label ?protein_label } core cell cycle protein

25 Linked Data Cloud

26 RDF Schema (RDFS) RDF Schema terms: –Class –Property –type –subClassOf –range –Domain Example:

27 Relational table -> RDF -> RDFS ontology

28 Web Ontology Language (OWL) It is more semantically expressive than RDF and RDFS, but it is syntactically the same as RDF –Relationship constraints such as cardinality, sameAs, etc It has three species: OWL Lite, OWL DL, OWL Full

29 OWL DL Representation (Subsumption) :Nucleus a owl:Class ; rdfs:subClassOf [ a owl:Restriction ; owl:onProperty :part_of ; owl:someValuesFrom :Cell ] Necessary but not sufficient condition: part of a nucleus is also part of a cell, but part of a cell is not necessarily part of a nucleus

30 OWL Reasoning Which proteins participate in “mitosis” :Protein a owl:Class ; rdfs:subClassOf [ a owl:Restriction ; owl:onProperty :participates_in ; owl:someValuesFrom :Mitosis ]

31 Semantic Web Rule Language (SWRL = OWL + Rules) hasParent(?x1,?x2) ∧ hasBrother(?x2,?x3) ⇒ hasUncle(?x1,?x3)

32 SW Enabling Technologies Ontology editor (e.g., protégé) Triple store (e.g., virtuoso) OWL reasoner (e.g., Pellet) SWRL reasoner (e.g., protégé plug-in)

33 Boimedical ontologies available in RDF/OWL format UniProt Gene Ontology NCI Metathesaurus Cell Ontology Sequence Ontology Protein Ontology These and many more ontologies are available in ontology repositories such as the NCBO BioPortal (

34 Applications of Ontologies

35 Siri

36 Google Knowledge Graph


38 Semantic Medline




42 Questions to be answered Patient P has a tumor recurrence with new mutations X and Y – which drugs should be used? In estradiol-treated SKBR3 cells, which nuclear protein complexes have the greatest change in phosphorylation? What is the largest number of genes one can knock out of Mycoplasma for it to remain viable?


44 The End

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